Integrating Wireless Sensor Networks and IoT for Intelligent Power Line Monitoring: A Theoretical Review of the Power Line Monitoring Systems Framework

Abstract

The persistent challenges of fault detection, downtime, and infrastructure vulnerability in sub-Saharan Africa's power grids necessitate a shift toward intelligent, distributed monitoring systems. This theoretical review investigates the foundational models and frameworks underpinning the Distributed Power Line Monitoring System (PoLiMoS), with a specific focus on enhancing fault detection, predictive maintenance, and operational resilience in sub-Saharan Africa's power grids, a wireless sensor and IoT-based platform designed for predictive maintenance and real-time monitoring of overhead power lines. It explores critical domains including Wireless Sensor Networks (WSNs), Internet of Things (IoT) architectures, predictive maintenance, smart grid reliability engineering, human–machine interaction, edge computing, and energy harvesting, to evaluate the feasibility of a decentralised, solar-powered, and cloud-integrated monitoring framework. The study critically evaluates each theoretical model’s strengths, limitations, and applicability within the PoLiMoS context, especially in rural or resource-constrained environments. Findings reveal that while robust theoretical support exists for decentralised sensing, cloud analytics, and edge intelligence, practical deployment demands adaptations in energy management, environmental resilience, and cybersecurity. Lightweight predictive models such as Bayesian Linear Regression are identified as more suitable for real-time edge processing in PoLiMoS compared to computationally intensive deep learning alternatives. Furthermore, the system's reliance on solar-powered sensor nodes and rugged hardware enclosures aligns with environmental adaptation theory for deployment in harsh climates. This review not only validates PoLiMoS’ multidisciplinary foundation but also highlights critical gaps in existing frameworks particularly in long-term reliability, cost-efficiency, and contextual scalability, paving the way for future hybrid architectures tailored to developing regions.